A global reference for caesarean section rates (C-Model): a multicountry cross-sectional study.

OBJECTIVE: To generate a global reference for caesarean section (CS) rates at health facilities. DESIGN: Cross-sectional study. SETTING: Health facilities from 43 countries. POPULATION/SAMPLE: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing. METHODS: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. MAIN OUTCOME MEASURES: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. RESULTS: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/). CONCLUSIONS: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. TWEETABLE ABSTRACT: The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems.

Changes in Caesarean Delivery Rates in Western Australia from 1995 to 2010 by Gestational Age at Birth.

BACKGROUND: The caesarean delivery rate in the developed world has been increasing. It is not well understood how caesarean delivery rates have changed by gestational age at birth in Western Australia, particularly in relation to the introduction of the early-term delivery guidelines in Australia in 2006. METHODS: Data from the Western Australian Midwives Notification System were used to identify 193,136 singletons born to primiparous women at 34-42 weeks' gestation during 1995-2010. Caesarean delivery rates were calculated by gestational age group (34-36 weeks, 37-38 weeks, and 39-42 weeks) and stratified into pre-labour and in-labour caesarean delivery. The average annual percent change (AAPC) for the caesarean delivery rates was calculated using joinpoint regression. Log-binomial regression was used to estimate the risk of having a caesarean delivery while adjusting for maternal and antenatal factors. RESULTS: Caesarean delivery rates rose steadily from 1995 to 2005 (AAPC = 5.9%, [95% confidence interval (CI) 4.9, 6.9]), but stabilised since then (AAPC = 0.9%, [95% CI -1.9, 3.8]). The rate of in-labour caesarean deliveries rose consistently from 1995 to 2010 across all gestational age groups. The pre-labour caesarean delivery rate rise was most dominant at 37-38 weeks' gestation from 1995 to 2005 (AAPC = 6.8%, [95% CI 5.4, 8.2]), but declined during 2006-10 (AAPC = -4.5, [95% CI -6.7, -2.3]), while at the same time the rate at 39-42 weeks rose slightly. CONCLUSIONS: The rise in pre-labour caesarean deliveries during 1995-2005 occurred predominantly at 37-38 weeks' gestation, but declined again from 2006 to 2010. This suggests that the recently developed Australian early-term delivery guidelines may have had some success in reducing early-term deliveries in Western Australia.

A global reference for caesarean section rates (C-Model): a multicountry cross-sectional study

Objective: To generate a global reference for caesarean section (CS) rates at health facilities. Design: Cross-sectional study. Setting: Health facilities from 43 countries. Population/sample: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing. Methods: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. Main outcome measures: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. Results: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/). Conclusions: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. (AU)

A global reference for caesarean section rates (C-Model): a multicountry cross-sectional study

ObjectiveTo generate a global reference for caesarean section(CS) rates at health facilities.DesignCross-sectional study.SettingHealth facilities from 43 countries.Population/SampleThirty eight thousand three hundred andtwenty-four women giving birth from 22 countries for modelbuilding and 10 045 875 women giving birth from 43 countriesfor model testing.MethodsWe hypothesised that mathematical models coulddetermine the relationship between clinical-obstetric characteristicsand CS. These models generated probabilities of CS that could becompared with the observed CS rates. We devised a three-stepapproach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population,building mathematical models, and testing these models.Main outcome measuresArea under the ROC curves, diagnosticodds ratio, expected CS rate, observed CS rate.ResultsAccording to the different versions of the model, areasunder the ROC curves suggested a good discriminatory capacityof C-Model, with summary estimates ranging from 0.832 to 0.844.The C-Model was able to generate expected CS rates adjusted forthe case-mix of the obstetric population. We have also preparedan e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/).ConclusionsThis article describes the development of a globalreference for CS rates. Based on maternal characteristics, this toolwas able to generate an individualised expected CS rate for healthfacilities or groups of health facilities. With C-Model, obstetricteams, health system managers, health facilities, health insurancecompanies, and governments can produce a customised referenceCS rate for assessing use (and overuse) of CS